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Colorimetric detection using microfluidic paper-based analytical devices (µPADs) and smartphones enable lowcost mobile chemical analysis solutions. However, variable illumination conditions and phone characteristics (i.e. camera hardware and software capabilities) limit the accurate interpretation and reproducibility of quantitative results. In this paper, we describe a method to automatically compensate an image captured by a smartphone camera under variable illumination conditions. By incorporating a two-step algorithm, we approximate the mobile camera picture color distribution to resemble a laboratory-grade measurement under reference illumination conditions. For every test image, the algorithm first applies a color mapping step that performs histogram matching of a set of color reference spots printed on the device to a laboratory reference measurement. After this initial correction step, a transformation matrix is computed via a least-square fit to minimize the differences between the device and the laboratory references. This matrix is then applied to the RGB channel values obtained from a µPAD to correct for illumination variations. The methodology was tested by correcting a test dataset captured using a smartphone to approximate to a calibration dataset acquired using a lab-grade camera. After correction, the relative error between the datasets fell to 10-20%, leading to an increase in classification accuracy between 12-33%. This approach enables colorimetric chemical analysis with smartphones outside the lab, removing the need to control external lighting conditions.

In this paper, we present an industrial application of multispectral imaging, for density measurement of colorants in photographic paper. We designed and developed a 9-band LED illumination based multispectral imaging system specifically for this application in collaboration with FUJIFILM Manufacturing Europe B.V., Tilburg, Netherlands. Unlike a densitometer, which is a spot density measurement device, the proposed system enables fast density measurement in a large area of a photo paper. Densities of the four colorants (CMYK) at every surface point in an image are calculated from the spectral reflectance image. Fast density measurements facilitate automatic monitoring of density changes (which is proportional to thickness changes), which helps control the manufacturing process for quality and consistent output. Experimental results confirm the effectiveness of the proposed system.

Mask manufacturers are facing major reticle metrology challenges, driving the need for cost‐effective solutions that provide tighter registration specifications and on‐device registration qualification. E‐beam mask writers’ local registration error may have a critical impact on the error contribution of the reticles to wafer overlay, as it is very local and most likely is not revealed with standard quality control schemes and sampling. The reticle error signatures are, of course, writing‐strategy‐dependent, but may also be caused by residual deflector alignment issues, thus leading to a very local but potentially critical non‐correctable overlay error on the wafer. Since the e‐beam writer strategy does not differ significantly between ArFi masks and EUV masks, we expect a similar error signature for both mask types. We will present data which demonstrates local registration errors that can be correlated to the writing swathes of state‐of‐the‐art e‐beam writers and multi‐pass strategies, potentially leading to systematic device registration errors versus design of close to 2nm. Furthermore, error signatures for local charging and process effects are indicated by local registration measurements resulting in systematic error, also on the order of 2nm. A unique measurement methodology, Local Registration Metrology, is presented that allows for dense sampling of reticle dies to characterize the local e‐beam registration error. Several thousand sites in a region of interest with length and width of a few hundred microns are measured. LMS IPRO metrology algorithms enable parallel registration measurement of all individual sites for fast and accurate metrology. High throughput allows completion of Local Registration measurement within a few minutes instead of exceeding a day of measurement time with conventional registration metrology strategies. This capability enables mask users to add local registration quality control to ensure the local wafer pattern placement error contribution by a mask is within the acceptable tolerance. In addition, based on the results of Local Registration Metrology, e‐beam mask writer corrections via feed forward can now be developed to significantly reduce local overlay error on wafer caused by the reticles.

In this paper, we demonstrate an automatic image analysis for Micro Total Analysis Systems (μTAS) or Lab-on-Paper (LoP). By placing fiducial markers on μTAS, application can crop image and remove bending distortions, also it can perform colorimetric analysis. With previously taken photo of μTAS device with applied reagents, user can determine ie. illness presence by uploading photo into the program. In the paper was prepared μTAS sheet, algorithm for finding fiducial points and software for colorimetric analysis written in Matlab.

There are several paradigms for color generation in reflective displays (e-Paper) including RGBW color filtering or stacked RGB or CMY. Theoretically, the highest white state reflectance and best color gamut are achieved by stacking three layers of pixels. However, stacking 3 layers induces significant optical losses especially at high resolution and typically does not allow for video operation. Therefore RGBW color filtering is currently preferred for higher resolution and single-layer e-Paper, but only provides color at 25% of the area and a maximum theoretical white reflectance of 50%. Presented herein is a new bi-primary color-system that cooperatively displays two complimentary colors inside a single sub-pixel, and therefore doubles the white state reflectance and color gamut for single-layer e-Paper. Also discussed are candidate e-Paper technologies that may be able to adopt the bi-primary color system, including possible advantages and challenges for each technology.

In order to consummate the management of bank paper, a new algorithm which can recognize the areas of paper tape in bundle of paper money is presented. Image segmentation is used to divide the image into several sub-blocks. Specifically, image entropy is computed in each sub-block. Experimental in different algorithms, including color training algorithm and the median filter and edge detection algorithm, demonstrates its real-time, effectiveness and robustness.

A methodology has been developed to digitize images from photographic color paper (reflection prints) and store the spectral information as a function of the three dye density basis functions inherent to the paper film type. The result is a highly accurate digitization of the spectral information at each spatial pixel that can be digitally stored with very little additional storage space over conventional RGB-type methodologies.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This report describes a method for the creation and automatic content verification of low-cost self-verifiable paper documents. The image of an original document is decomposed to symbol templates and their corresponding locations. The resulting data is further compressed and encrypted, and encoded in custom designed high-capacity color barcodes. The original image and barcodes are printed on the same paper to form a self-verifiable authentic document. During content verification, the paper document is scanned to obtain the barcodes and target image. The original image is reconstructed from data extracted from the barcodes, which is then registered with and compared to the target image. The verification is carried out hierarchically from the entire image down to word and symbol levels. For symbol level comparison, multiple types of features and shape matching are utilized in a cascade. The proposed verification method is inexpensive, robust and fast. Evaluation on 216 character tables and 102 real documents achieved greater than 99% alteration detection rate and less than 1% false positives at the word/symbol level.

In this work, two optical systems previously designed and implemented by our research team, were used to enable the surface and bulk inspection of the ink-paper interaction by image analysis. Basically, the first system works by ejecting micro-liter ink drops onto the papers surface while monitoring the event under three different views over time. The second system is used for sectioning the paper samples through their thickness and to simultaneously acquire images of the ink penetration of each section cut. In the performed experiments, three black inks of different brands and a common copy paper were chosen, used, and tested with the two developed optical systems. Both qualitative and quantitative analyses were carried out at the surface level and in the bulk of the paper. In terms of conclusions, it was shown that the three tested ink-paper combinations revealed very distinct characteristics.

Wet paper code is a complex model which mainly used in the field of image coding. This paper is based on the wet paper code model and human visual system, and constructs a new wet paper code steganographic method. According to the regional complexity and other characteristics of the host image, the secret bits are adaptively embedded into wavelet coefficients of image subbands with wet paper code. Secret information receivers do not need to know the specific method of secret writing, just do a simple matrix multiplication operation and can extract the secret information, which in many ways to improve the security of the steganographic algorithm. The experiments show that the method has good visual invisibility and resistance of active steganalysis attacks.

Over the last decade DigitalGlobe (DG) has built and launched a series of remote sensing satellites with steadily increasing capabilities: QuickBird, WorldView-1 (WV-1), and WorldView-2 (WV-2). Today, this constellation acquires over 2.5 million km2 of imagery on a daily basis. This paper presents the configuration and performance capabilities of each of these satellites, with emphasis on the unique spatial and spectral capabilities of WV-2. WV-2 employs high-precision star tracker and inertial measurement units to achieve a geolocation accuracy of 5 m Circular Error, 90% confidence (CE90). The native resolution of WV-2 is 0.5 m GSD in the panchromatic band and 2 m GSD in 8 multispectral bands. Four of the multispectral bands match those of the Landsat series of satellites; four new bands enable novel and expanded applications. We are rapidly establishing and refreshing a global database of very high resolution (VHR) 8-band multispectral imagery. Control moment gyroscopes (CMGs) on both WV-1 and WV-2 improve collection capacity and provide the agility to capture multi-angle sequences in rapid succession. These capabilities result in a rich combination of image features that can be exploited to develop enhanced monitoring solutions. Algorithms for interpretation and analysis can leverage: 1) broader and more continuous spectral coverage at 2 m resolution; 2) textural and morphological information from the 0.5 m panchromatic band; 3) ancillary information from stereo and multi-angle collects, including high precision digital elevation models; 4) frequent revisits and time-series collects; and 5) the global reference image archives. We introduce the topic of creative fusion of image attributes, as this provides a unifying theme for many of the papers in this WV-2 Special Session.

At present, the most technology of counting money is to use the money counter in financial fields. The paper presents a new method for automatic counting paper money which is based on image processing technology. Firstly, the paper money image is acquired by CCD. After analyzing the feature of image, we find that in Cr-space the edge of each paper money is enhanced. Then we use the north-west sobel operator for filtering and north sobel operator for detecting edge. Although the image-processed better highlight the edge of each paper money, the edge is rough and its variance is high. It is hardly to threshold the image for getting the single-pixel edge linked. After Different segmentation algorithm was been used for deriving the edge of paper money, we find the Two-dimensional Histogram θ-division algorithm is suitable for our purpose. The experimental result is proved satisfied. The detecting rate reached 100% in controlled environment for RMB. However, if we want to detect other kinds of paper money such as dollar, there also have several problems to be solved.

This paper presents an algorithm called CIPDEC (Content Integrity of Printed Documents using Error Correction), which identifies any modifications made to a printed document. CIPDEC uses an error correcting code for accurate detection of addition/deletion of even a few pixels. A unique advantage of CIPDEC is that it works blind - it does not require the original document for such detection. Instead, it uses fiducial marks and error correcting code parities. CIPDEC is also robust to paper-world artifacts like photocopying, annotations, stains, folds, tears and staples. Furthermore, by working at a pixel level, CIPDEC is independent of language, font, software, and graphics that are used to create paper documents. As a result, any changes made to a printed document can be detected long after the software, font, and graphics have fallen out of use. The utility of CIPDEC is illustrated in the context of tamper-proofing of printed documents and ink extraction for form-filling applications.

An important print quality attribute of digital printing equipment deals with the absolute position of the printed image relative to the page. Historically, the most precise method of measuring image to paper (IOP) registration is by scanning a printed sheet on a flatbed scanner. These measurements have been limited to sheets smaller than the full capacity of the printer. In addition, the precision of the measurement has been limited by the accuracy of the scanner itself and the measurement of a few (~4) points on the page have limited the information that can be gathered. The new method proposed in this paper measures IOP registration throughout the sheet in a more precise manner. In a similar fashion, the relative position of the image on both the simplex and duplex side of the print can be determined. In addition, the new method helps link the source of registration errors to individual sub-systems. By generating the individual error sources from a printed sheet enables the understanding of the percentage contribution of each sub-system, prioritizes efforts to obtain better IOP performance, finds initial IOP setup errors of a printing engine, compares different technologies affecting IOP registration in sub-systems and potentially acts as a diagnostic tool for individual sub-systems.

Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for specifying the quality potential of printers and materials in more detail than before. Both production and end-use standpoints are relevant. This paper gives an overview of an on-going study which has the goal of determining a framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is founded on the concept of a layered network model. The model and its subjective, objective and instrumental measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image content has a marked effect on the dimensions. This underlines the challenges in designing the test images.

Annotation watermarking (sometimes also called caption or illustration watermarking) denotes a specific application of watermarks, which embeds supplementary information directly in the media, so that additional information is intrinsically linked to media content and does not get separated from the media by non-malicious processing steps such as image cropping or compression. Recently, nested object annotation watermarking (NOAWM) has been introduced as a specialized annotation watermarking domain, whereby hierarchical object information is embedded in photographic images. In earlier work, the Hierarchical Graph Concept (HGC) has been suggested as a first approach to model object relations, which are defined by users during editing processes, into a hierarchical tree structure. The original HGC method uses a code-book decomposition of the annotation tree and a block-luminance algorithm for embedding. In this article, two new approaches for embedding nested object annotations are presented and experimentally compared to the original HGC approach. The first one adopts the code-book scheme of HGC using an alternative embedding based on Wet Paper Codes in blue-channel LSB domain, whereas the second suggests a new method based on the concept of intrinsic signal inheritance by sub-band energy and phase modulation of image luminance blocks. A comparative experimental evaluation based on more than 100 test images is presented in the paper, whereby aspects of transparency and robustness with respect to the most relevant image modifications to annotations, cropping and JPEG compression, are discussed comparatively for the two code-book schemes and the novel inheritance approach.

The whiteness level of a printing paper is considered as an important quality measure. High paper whiteness improves the contrast to printed areas providing a more distinct appearance of printed text and colors and increases the number of reproducible colors. Its influence on perceived color rendering quality is however not completely explained. The intuitive interpretation of paper whiteness is a material with high light reflection for all wavelengths in the visual part of the color spectrum. However, a slightly bluish shade is perceived as being whiter than a neutral white. Accordingly, papers with high whiteness values incline toward bluish-white. In paper production, a high whiteness level is achieved by the use of highly bleached pulp together with high light scattering filler pigment. To further increase whiteness levels expensive additives such as Fluorescent Whitening Agents (FWA) and shading dyes are needed. During the last years, the CIE whiteness level of some commercial available office paper has exceeded 170 CIE units, a level that can only be reached by the addition of significant amounts of FWA. Although paper whiteness is considered as an important paper quality criterion, its influence on printed color images is complicated. The dynamic mechanisms of the human visual system strive to optimize the visual response to each particular viewing condition. One of these mechanisms is chromatic adaptation, where colored objects get the same appearance under different light sources, i.e. a white paper appears white under tungsten, fluorescent and day light. In the process of judging printed color images, paper whiteness will be part of the chromatic adaptation. This implies that variations in paper whiteness would be discounted by the human visual system. On the other hand, high paper whiteness improves the contrast as well as the color gamut, both important parameters for the perceived color reproduction quality. In order to quantify the influence of paper whiteness pilot papers with different amount of FWA but in all other respects similar were produced on a small scale experimental paper machine. The fact that only the FWA content changes reduces the influences of other properties separated from the paper whiteness in the evaluation process. A set of images, all having characteristics with the potential to reveal the influence of the varied whiteness level on color reproduction quality, were printed on the pilot papers in two different printers. Prior to printing the test images in the experiment, ICC-profiles were calculated for all the used printer-substrate combinations. A visual assessment study of the printed samples was carried out in order to relate the influence of the paper whiteness level to perceived color reproduction quality. The results show an improved color rendering quality with increased CIE whiteness value up to a certain level. Any further increase in paper whiteness does not contribute to an improved color reproduction quality. Furthermore, the fact that some printing inks are UV blocking while others are not will introduce a non uniform color shift in the printed image when the FWA activation changes. This non uniform color shift has been quantified both for variations in illuminant as well as variations of FWA content in the paper.

This study is focused on laser structuring processes for producing secure tactile features on paper-based security documents. The feasibility of using the developed technique allows users and/or machines to discriminate among genuine and counterfeited security documents and/or facilitate the tactile detection by visually impaired people.

In this paper a new method is proposed to control a vision-based robot in large navigation spaces. In this case, visual features observed by an on-board camera can change drastically or even disappear completely between the initial image, as seen at the beginning of a task, and the final image, as seen at the desired position of the robot. These features are therefore not suffcient for controlling the entire motion of the robotic system from beginning to end. This problem requires a more complete definition and representation of the navigation space. This can be achieved by a topological representation, where the environment is directly defined in the sensor space by a data-base of images. In our approach, this data-base is acquired during an offline learning step. An image retrieval method then indexes and matches a request image, given by the camera, to the closest view within the data-base. In this way, an image path is extracted from the database to link the initial and desired images providing enough information to control the robot. The central point of this paper is focused on the closed-loop control law that drives the robot to its desired position using this image path. The method proposed does not require either a global reconstruction or a temporal planning step. Furthermore, the robot is not obliged to converge directly upon each image waypoint but chooses automatically a better trajectory. The visual servoing control law designed uses specific features which ensure that the robot navigates within the visibility path. Experimental simulations are given to show the effectiveness of this method for controlling the motion of a camera in three-dimensional environments (free-flying camera, or camera moving on a plane).

An automatic recognition system for Paper Currency Numbers was developed in this paper. The paper currency number can be recognized and recorded by this system at the same time of binding. At First, an image of 8 bits grey-scale was acquired by CCD camera, and then some output number clusters can be through process of segmentation by Grey Ridge-Valley algorithm, orientation by projection, and character recognition by structure-analyzing algorithm. Results of experiments demonstrated that the proposed algorithm of gray ridges and valleys can extract feature effectively, the algorithm of distance of traversing number body orientation acute, and this system achieves a high recognition rate and a fast recognition speed. It still reached the practical degree. The system has applied national patent.

High-resolving full-field OCT method is considered that provides increased resolution and interferometric data acquisition speed due to high optical magnification and electronic lateral scan provided by video camera. OCT data processing algorithm based on signal squaring with subsequent low-pass filtering is considered. Experimental results obtained when evaluating samples of paper material are presented and discussed.

A device has been designed for off-line optical paper inspection and quality control of stripes and holes in the cigarette paper. Hardware description is first presented including main paper characteristics to be measured. Typical paper stripe and holes structures are then discussed with image processing and analysis considerations to discriminate these structures, focusing in the problems derived from the small area of holes and of their internal structure that is analyzed with a confocal microscope. Algorithms for image processing and analysis are described. These algorithms involve equalization, binarization, stripes structure detection, holes distribution and statistics.

A non-supervised clustering based method for classifying paper according to its quality is presented. The method is simple to train, requiring minimal human involvement. The approach is based on Self-Organizing Maps and texture features that discriminate the texture of effectively. Multidimensional texture feature vectors are first extracted from paper images. The dimensionality of the data is then reduced by a Self-Organizing Map (SOM). In dimensionality reduction, the feature data are projected to a two-dimensional space and clustered according to their similarity. The clusters represent different paper qualities and can be labeled according to the quality information of the training samples. After that, it is easy to find the quality class of the inspected paper by checking where a sample is placed in the low-dimensional space. Tests based on images taken in a laboratory environment from four different paper quality classes provided very promising results. Local Binary Pattern (LBP) texture features combined with a SOM-based approach classified the test data almost perfectly: the error percentage was only 0.2% with the multiresolution version of LBP and 1.6% with the regular LBP. The improvement to the previously used texture features in paper inspection is huge: the classification error is reduced over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view to the inspected data.

This paper introduces tracking control system with visual feedback to a moving object by using the measurement device which we developed. In order to recognize the moving object, we use two method, using cross-shape mark and Orientation Code Matching (OCM). And the measurement device is constructed PID control system with Extended Kalman Filter in order to track to object. Through the several experiments, we verify the percormance of recognition and tracking.

The benefits of stereo vision are numerous and quickly become apparent when attempting to perform simple tasks without the aid of stereo cues. The proliferation of remotely operated vehicles and indirect viewing applications has resulted in an increased need to see the operational environment in stereo. Several approaches to the display of stereo imagery have been demonstrated. Stereoscopic displays typically require the user to wear special headgear. Autostereoscopic displays require no headgear but typically have tight limitations on the position of the viewer's head. Previous papers have described a new type of stereoscopic display based on dual liquid crystal displays. The new display provides a stereo view without temporal or spatial multiplexing. Several prototype displays have been built and demonstrated to potential users with favorable reviews. With a successful proof-of-principle prototype completed, the effort is focusing on improvements to the image quality, consolidating the electronics, and packaging the display for use in rugged environments. The results of this effort should produce a rugged, compact, multiuse stereo display.

Over the last two decades, researchers investigated various approaches for detection and classification of targets in forward looking infrared (FLIR) imagery using correlation based techniques. In this paper, a novel technique is proposed to recognize and track single as well as multiple identical and/or dissimilar targets in real life FLIR sequences using a combination of extended maximum average correlation height (EMACH) and polynomial distance classifier correlation filter (PDCCF). The EMACH filters are used for the detection stage and PDCCF filter is used for the classification stage for improving the detection and discrimination capability. The EMACH and PDCCF filters are trained a priori using target images with expected size and orientation variations. In the first step, the input scene is correlated with all the detection filters (one for each desired or expected target class) and the resulting correlation outputs are combined. The regions of interest (ROI) are selected from the input scene based on the regions with higher correlation peak values in the combined correlation output. In the second step, PDCCF filter is applied to these ROIs to identify target types and reject clutters/backgrounds based on a distance measure and a threshold. Moving target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in few frames and then reappearing in later frames. This method has been found to yield robust performance for challenging FLIR imagery in terms of faster and accurate detection and classification as well as tracking of the targets.

Application of colorimetry in color identification for checking electronic components and quality of pulp paper is proposed. Inspection of missing electronic components during the assembly of the printed circuit board and dirty portions on pulp paper can be done by an analysis of color image. This method helps to improve the image-processing speed. Additionally, the inspection accuracy can be attained by the use of color iso discrimination contour criterion.

The human visual system has an amazing sensitivity-even a single photon catch can trigger the release of a signal in a rod photoreceptor cell under certain circumstances. However, behaviorally it requires on an average 5-8 photons for a human to "see" a flash of light. This discrepancy is due to the intrinsic "dark noise" in the visual system. Various aspects of human visual sensitivity to single photons are reviewed and discussed.

A 128(H) x 64(V) x RGB CMOS imager is integrated with region-of-interest selection, RGB-to-HSI transformation, HSI-based pixel segmentation, 36-bins x 12bits HSI histogramming and sum-of-absolute-difference (SAD) template matching. 32 learned color templates are stored and compared to each image. The chip captures the R, G and B images using in-pixel storage before passing the pixel content to a multiplying digital-to-analog converter (DAC) for white balancing. The DAC can also be used to pipe in images for a PC. The color processing uses a biologically inspired color opponent representation and an analog look-up table to determine the Hue (H) of each pixel. Saturation (S) is computed using a loser-take-all circuit. Intensity (I) is given by the sum color components. A histogram of the segments of the image, constructed by counting the number of pixels falling into 36 Hue intervals of 10 degrees, is stored on chip and compared against the histograms of new segments using SAD comparisons. We demonstrate color-based image segmentation and object recognition with this chip. Running at 30fps, it uses 1mW. To our knowledge, this is the first chip that integrates imaging, color segmentation and color-based object recognition at the focal plane.

True 3D displays, whether generated by volume holography, merged stereopsis (requiring glasses), or autostereoscopic methods (stereopsis without the need for special glasses), are useful in a great number of applications, ranging from training through product visualization to computer gaming. Holography provides an excellent 3D image but cannot yet be produced in real time, merged stereopsis results in accommodation-convergence conflict (where distance cues generated by the 3D appearance of the image conflict with those obtained from the angular position of the eyes) and lacks parallax cues, and autostereoscopy produces a 3D image visible only from a small region of space. Physical Optics Corporation is developing the next step in real-time 3D displays, the automultiscopic system, which eliminates accommodation-convergence conflict, produces 3D imagery from any position around the display, and includes true image parallax. Theory of automultiscopic display systems is presented, together with results from our prototype display, which produces 3D video imagery with full parallax cues from any viewing direction.

A unified way of detecting and tracking moving targets with a SAR radar called SAR-MTI is presented. SAR-MTI differs from STAP or DPCA in that it is a generalization of SAR processing and can work with only a single phase center. SAR-MTI requires formation of a series of images assuming different sensor ground speeds, from vs-vtmax to vs+vtmax, where vs is the actual sensor ground speed and vtmax is the maximum target speed of interest. Each image will capture a different set of target velocities, and the complete set of images will focus all target speeds less than a desired maximum speed regardless of direction and target location. Thus the 2-dimensional SAR image is generalized to a 3-dimensional cube or stack of images. All linear moving targets less than the desired speed will be focused somewhere in the cube. The third dimension represents the along track velocity of the mover which is a piece of information not available to standard airborne MTI. A mover will remain focused at the same place within the cube as long as the motion of the mover and the sensor remain linear. Because stationary targets also focus within the detection cube, move-stop-move targets are handled smoothly and without changing waveforms or modes. Another result of this fact is that SAR-MTI has no minimum detectable velocity. SAR-MTI has an inherent ambiguity because the four-dimensions of target parameters (two dimensions in both velocity and position) are mapped into a three-dimensional detection space. This ambiguity is characterized and methods for resolving the ambiguity for geolocation are discussed. The point spread function in the detection cube is also described.

We present an overview of signal and image processing techniques developed for the concealed weapon detection (CWD) application. The signal/image processing chain is described and the tasks include image denoising and enhancement, image registration and fusion, object segmentation, shape description, and weapon recognition. Finally, a complete CWD example is presented for illustration.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The use of wastepaper as a raw materials within the paper making industry is steadily increasing. One of the key elements in processing wastepaper is the removal of ink. Current research in the industry is focused on the capability of measuring particle size on-line, within a paper mill environment. This paper describes the development of a prototype machine vision system for the analysis of ink particles within wastepaper pulp samples. A priori knowledge of the domain has been exploited to produce a lighting and sample presentation system which maximizes ink particle contrast within the image. The system has been used to examine samples from a pilot deinking cell at various stages of operation. This gives a wide spectrum of sample compositions typically encountered within a mill environment. One of the key areas in a fully automated analysis system is the segmentation of the images to determine ink particle content. Simple thresholding techniques provide the fast solution required to analyze the high throughput of a deinking line. Due to the large variation in image content the development of an overall segmentation strategy incorporating several algorithms is proposed. A suitable segmentation process is selected according to a priori knowledge of the sample content and speed of operation required. The performance limitations for particular algorithms have been determined to aid the selection process.

Earlier studies of the human vision system suggest that a complex object may be recognized from the qualitative features associated with its boundary. Since the human vision system uses qualitative features for object recognition, it is extremely robust to deformation caused by noise, obstruction, scale change, or optical distortion. We considered various qualitative descriptors for digital images. The descriptors developed in this research are based on the estimated curvature function from digital boundaries and can discriminate features such as straightness of a contour segment, perpendicularity of two contour segments, parallelness of two straight contour segments, parallelness of two curved contour segments in a global sense, and the direction of convergence if two straight contour segments are not parallel. We demonstrated that the qualitative descriptors developed in this paper can be applied for identification of elementary shapes, such as cylinders, bricks, cones, etc. We also discussed the recognition of more complex shapes after decomposing them into simpler components.

By examining aspherical surface aberration contributions it is shown that aspheres can be used to simplify zoom lens construction and to improve image quality by allowing the designer to selectively correct aberrations at different zoom positions. 1.

The goal of the research described in this paper is the design of an intelligent computer vision system to automatically extract information from paper-based maps and answer queries related to spatial features and structure a geographical data. The foundation to such a system is a set. of powerful image analysis algorithms to operate on digitized images of paper-based maps. Efficient algorithms to orient map images, detect symbols, identify and track various types of lines, follow closed contours, compute distances, find shortest paths, etc. have been developed. An intelligent query processor analyzes the queries presented by the user in a predefined syntax, controls the operation of the image processing algorithms, and interacts with the user. The query processor is written in LISP and calls image analysis routines written in FORTRAN. In this paper we describe the image analysis algorithms and present their effectiveness in extracting information from a simplified map image of 2,048 x 2,048 pixels.© (1988) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Rotation-invariant pattern recognition is shown to have intrinsic limitations determined by the set of patterns to be recognized and by the specific optical setup. Within these limitations, a general procedure is proposed for the generation of bipolar filters that do not require the sensitive alignment procedures involved in holographic filters and are suitable for superposition synthesis to achieve rotation invariance.

Chinese wines can be classification or graded by the micrographs. Micrographs of Chinese wines show floccules, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs, we study the classification of Chinese wines based on the micrographs. Shape and structure of wines' particles in microstructure is the most important feature for recognition and classification of wines. So we introduce a feature extraction method which can describe the structure and region shape of micrograph efficiently. First, the micrographs are enhanced using total variation denoising, and segmented using a modified Otsu's method based on the Rayleigh Distribution. Then features are extracted using proposed method in the paper based on area, perimeter and traditional shape feature. Eight kinds total 26 features are selected. Finally, Chinese wine classification system based on micrograph using combination of shape and structure features and BP neural network have been presented. We compare the recognition results for different choices of features (traditional shape features or proposed features). The experimental results show that the better classification rate have been achieved using the combinational features proposed in this paper.

At present, commonly endmember extraction of hyperspectral is mainly concentrated in the spectral region. Because the spatial information is not used enough, the endmember extraction is not precise which can lead to a bad result of mixed pixel decomposition and hyperspectral target detection. Actually, the distribution of endmembers in space has a certain shape and aggregation. By making use of these information we can extract more precise endmembers. Automatic morphological endmember extraction technology can make full use of abundant spectral information and spatial information. This paper based on the existed automatic morphological algorithm, presents a method in combination with maximum distance for morphological endmember extraction to solve the influence of spectral variations, which effectively extracts different classes of endmember curves. Based on the theory of orthogonal subspace projection, the authors propose an improved constrained energy minimization (CEM) algorithm, achieve better hyperspectral target detection results.

A model has been developed to compare the system sharpness of continuous and discrete imaging systems. The continuous system consists of the traditional photographic process including the camera lens the film the printer lens and the photographic paper. The discrete system is an electronic still camera (ESC) and is built around a CCD imaging sensor and a laser printer the camera lens and the photographic paper are the same for both systems. The model uses CMT acutance to measure sharpness and introduces the concept of using the ratio of the aliased signal to the non-aliased signal as a measure of the probability of system aliasing. The model allows the user to study the trade offs between system sharpness and aliasing as a function of sensor resolution pixel size and the nature of the optical anti-aliasing pre-filter used in the ESC. The results of the model indicate that when a well-defined optical anti-aliasing pre-filter is made from a birefringent filter then when the pre-filter introduces a shift of about one-half the pixel pitch of the sensor then the potential for aliasing is minimized. For a given sensor resolution as the pixel size is reduced the potential for aliasing increases as does the system sharpness and when the potential for aliasing is minimized by use of the pre-filter the smaller pixel size still results in a greater potential for aliasing than for the pixels. For a given resolution it is possible to move from the Frame Transfer format to an Interline Transfer format and have the same system sharpness and potential for aliasing thus allowing flexibility in system design but the potential for aliasing is greater than the minimum value associated with the Frame Transfer format. As the system resolution increases the minimum in the potential for aliasing is more sharply defined as a function of the image shift introduced by the pre-filter. Also the potential for aliasing drops off sharply as the sensor resolution increases. In the case of the ESC system the number of lines used to form the final print can have a very large impact on the system sharpness and the amount of aliasing seen by the observer. 1.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An experimental high resolution cathode ray tube (CRT) printer was developed to produce high quality 3. 5in. x 5 in. and 4 in. x 6 in. photographic prints. A monochrome CRT utilizing red green and blue filtration was used to sequenlially expose continuous-tone color images directly onto conventional photographic paper. A summary of the design and implementation of the printer at the systems level will be presented. The design approach taken to meet the system quality requirements is described from the electrical mechanical and optical points of view. In addition the presentation will emphasize the design challenges of implementing a CRT imager and the solutions to the unique problems it presents. Other concerns discussed in detail include spatial resolution and bit resolution image size and magnification colorimetry and paper response matching. Sample prints of test patterns and scanned images will be shown. 1.

This paper based on a number of optical transfer function(0TF) experiments, has given a criterion which used for the evaluation on image-forming quality of camera lenses. And it put forward a proposal for further research and approach of getting an effective measures on OTF evaluation. It also describes an important factor which lead to the off-axis unsymmetrical error on OTF testing.

With the wide application of micro-nano structures in various fields, micro-nano technology has been developed continuously. Among them, micro-nano surface morphology detection technology has become a research hotspot, because the surface geometry and morphology of micro-nano devices determine the functional level and service life of devices. With the increasing complexity of micro nano device surface morphology and the increasing sharp edges, the detection technologies are facing severe challenges. Among various detection methods, structured illumination microscopy (SIM) has attracted many research interests due to the characteristics of high accuracy, strong adaptability and high efficiency. The existing SIM are mainly based on phase-shift technique, Hilbert transform technique and global Fourier transform technique. However, for complex-surface and steep-edge measurements, it is difficult for traditional SIM to achieve both high accuracy and high efficiency. In this paper, a method based on SIM which combines vertical scan and phase-shift is proposed. In this proposed measurement system, vertical scanning of the object is synchronized with the switching of the phase-shifted fringe pattern and only one fringe pattern needs to be projected, which enables a point-to-point processing defined as local Fourier transform method in this paper to be utilized to extract the modulation information which will reserve high-frequency information of the image so it can be applied to both smooth and rough surfaces. Simulation and experiment are carried out to demonstrate that the proposed method can successfully realize fast and accurate detection of complex-surface and steep-edge.

This paper combines the information processing technology for target detection with the sensor parameter design. A design method which is suitable for the staring infrared sensor in geosynchronous orbit has been proposed. The signal-to-noise ratio (SNR) has been introduced to evaluate the detection performance. The sensor temporal-spatial noise and background clutter time-domain residual have been comprehensively analyzed. First, the temporal noise and the spatial noise have been separated and compared, and then the background clutter and time-domain residual with different ground sample distance (GSD) have been analyzed based on the on-orbit remote sensing image. The results showed that selecting the appropriate GSD and applying the target detection method of time domain correlation, under the background-limited photodetection (BLIP), the design threshold of the noise equivalent target (NET) can be improved, and the detection and information processing efficiency can be maximized. The method proposed in this paper provide information for the design and optimization of space-based staring infrared sensor parameter.

This paper proposes a large depth-of-field (DOF) imaging method, which uses an image acquisition system equipped with a liquid lens. This paper also establishes the relationship between the focusing distance and the current value to estimate the object distance. The proposed method firstly uses a liquid lens to capture multi-focus images. Then the different objects in the scene are segmented by image processing algorithm. Then we apply an effective blur estimation algorithm on each object to determine the clearest image from all multi-focus images and obtain the corresponding current value. Finally, the clear images of different objects are combined to obtain a large DOF image, and meanwhile, the distances of different objects can be estimated, respectively. Experimental results show that the proposed method can effectively achieve large DOF imaging and measure approximate object distances.

High-resolution 3D optical imaging is important for unmanned underwater vehicles (UUVs) in the applications of target detection and recognition, underwater engineering, automatic navigation, scientific research, and natural resources exploration. Compared with stereo camera and 3D laser scanning imaging, 3D range-gated imaging (3D RGI) has longer detection range and higher spatial resolution at the same time. This paper presents a survey on 3D range-gated imaging methods for underwater detection. Up to now, there are two main methods developed, including time slicing method and gated range intensity correlation method. The literature about 3D RGI has been reviewed in different detection applications for UUV. We also introduce our works in 3D gated range-intensity correlation imaging for fishing net detection and marine life in-situ detection. This paper is beneficial for the 3D RGI technique in underwater detection applications for UUV.

Compared with images taken in the air, underwater images are susceptible due to the adverse effects of complex water conditions, such as absorption, attenuation, scattering, various noises, and uneven lighting, etc. These effects severely reduce the quality of underwater imaging and give rise to many problems among which is image color distortion. To fully acknowledge the underwater scenes, it is important to acquire the images with accurate colors, especially for marine rescue, underwater target recognition, and marine ecological research. To address this problem, this paper proposes an underwater imaging technology based on a light field camera array. The application of this technology can not only perform accurate color correction processing on the captured images but also expand the dynamic range of underwater images, thereby obtain enhanced high-quality, true-color underwater images. The experimental system in this paper contains a 3*3 camera array that simultaneously captures images of underwater targets with different exposure times. As the light attenuation coefficient of different wavelengths in water varies with the wavelength, during the color restoration process, we measure the relative value of the underwater spectral power distribution concerning the air, calculate the tristimulus value change according to the colorimetric principle, and convert the tristimulus value change in the CIEXYZ color space to the CIE La*b* color space. The color on the underwater images obtained by the camera array is then compensated. Then, in the image fusion process, images with different exposure are used to recover the response function of the imaging process, and multiple images are fused into a single, high-dynamic-range radiance map using high quality tone mapping technique in the radiance value dynamic display high-contrast images on devices with limited range. Experimental results show that our light-field imaging color correction and dynamic range expansion processing methods have obvious advantages over single cameras in underwater color correction and underwater moving target imaging detection, which provide advanced technology and equipment for underwater in-situ applications.

This paper proposed an infrared and visible image fusion algorithm based on three-layer guided filter and a Composition Analysis Convolutional Neural Network (CACNN) to address the problems of background information not being delicate and some details being lost in the images fused by the traditional infrared and visible image fusion algorithm. Firstly, the original image is decomposed into the base layer and the detail layer through the multi-scale decomposition method of three-layer guided filter. Then, the CACNN model and the regional energy method are utilized to guide the fusion of the base and detail layers. Finally, the fused image is obtained by merging the base and detail layers. Experimental verification shows that compared with similar algorithms, the proposed model in this paper has a great improvement in evaluation metrics such as Average Gradient (AG) and Spatial Frequency (SF), and better retained and presented the detailed texture information of the images.

Object detection is a hot talking point in computer vision. Recently, as COVID-19 is spreading globally, the epidemic prevention and control has entered a normalization, wearing masks when entering and leaving public places and taking public transportation has now become normalized. The recognition of face mask is also of increasing concern. Then fast and accurate mask identification is essential. The Faster R-CNN is currently a more advanced object detection algorithm. It has the advantages of fast detection speed and high detection accuracy and is widely used in various fields. However, this method often fails to demonstrate its excellent performance in detecting small objects. This paper is based on the Faster R-CNN object detection algorithm and introduces FPN to solve multi-scale mask recognition and detection. The feature map of each resolution is introduced into the latter resolution feature map for element-wise summation operation. Fusing shallow layers with high resolution and deep layers with rich semantic information to improve the ability to detect small objects. The method is validated with 2000 face mask images as the dataset. Experimentally, the improved method proposed in this paper proves to be effective and better than the original algorithm. © 2021 SPIE.

Image stitching adopts image registration and image fusion technology, which combines several images with overlapping areas into a wide-view, high-resolution panoramic image. Image Stitching is widely used in remote sensing image processing, medical diagnosis, military detection and many other fields. When the image is stitched, the feature points extracted by methods based on grayscale, frequency domain and image features are too redundant, and the feature points are not comprehensive enough. In order to solve the above problems, this paper proposes a network framework for extracting feature points of image stitching based on deep learning, which is called SA-LIFT. First, the image is divided into similar and non-similar areas by extracting model. Second, an improved LIFT model is proposed, which uses three convolutional networks to extract image feature points. The experimental results show that the method used in this paper has good effects in improving comprehensiveness and reducing redundancy when extracting the feature points of image stitching.